Dhivya
18/08/2020
## Objective :-
##
## > To understand the school closures across the globe due to the pandemic.
## > To study if there is significant co-relation between School Closures and any of the following:
## * Enrolment in public schools
## * Income levels of the countries
## * Geographical impact
## > Understanding the impact of income levels on education also contribute to a major part of this analysis.
## Data Source :-
##
## This is an open source dataset published in Kaggle.
## This data was provided by UNESCO and was captured from Jan-2020 to April 2020, across several countries.
## Illustration and observations were arrived based on the dataset on Education and COVID.
## Data Description :-
##
## The Dataset contains information about the closure of schools around the globe such as status and date of closing.
## It also contains the No. of students enrolled in various levels of school around the globe in various countries.
## Figures correspond to the number of learners enrolled at pre-primary, primary, secondary as well as
## at tertiary education levels.
Dataset :- https://www.kaggle.com/landlord/education-and-covid19
## Inference :-
## 1. School Closures does not seem to be based on the geographic location.
## 2. There is a mix of school closures across the different regions.
## Inference
## 1. Majority of the countries across the globe fall under the high-income category.
## 2. Mean enrolments in Public schools across the globe :- 9615947
## 3. Median of enrolments in Public schools across the globe :- 1885226
## 4. Most of the countries have closed their schools due to the pandemic.
## Inference
## 1. Most of the European countries fall under the high-income category.
## 2. All the North American countries fall under the high-income category.
## 3. The low-income countries are primarily the countries in Saharan Africa.
## 4. The income levels in South Asia ranges closely between the upper-middle-income to low-income categories.
## 5. The number of South Asian countries in the low-income & upper-middle-income are nearly the same.
## Inference
## 1. Mean enrolment in schools is hightest in South Asia. This could be due to the outlier with maximum enrolments.
## 2. The mean of enrolments in South Asia and North America are similar.
## However, the mean in the South Asian lower-middle-income category is significantly higher than North Americas.
## 3. Europe, having significantly more number of countries in the high-income category have
## the least enrolment in schools across the globe.
## 4. The high-income countries in the Saharan Africa have the lowest enrolment in schools.
## Inference
## 1. Latin America tops the school closures due to the pandemic, followed by the countries in Saharan Africa.
## 2. All the schools in South Asia were closed due to the pandemic.
## 3. European schools, especially, from a considerable number of high-income countries,
## have remained open with limitations.
## Could this be an indicator that the high-income countries pursue a stronger economy?
## Inference
## 1. School Closure started as early as January in East Asia, indicating the outbreak of COVID in China.
## 2. School Closures were at a peak, in March 2020, synonymous with the lockdown.
## 3. While most countries shut down their schools in March, several countries in Eurpoe and Central Asia,
## and quite a few countries in Saharan Africa have been operating with limitations.
## Inference
## 1. The size of the circles indicate the number of enrollments.
## 2. With high enrolments in the South Asian regions and moderate enrolments in South American countries,
## schools have remained closed.
## 3. African region have nominal enrolments & varied school closure statuses.
## 4. European region have low to moderate enrolments and most schools have remained open
## (most of them, with limitations).
## 5. Schools in East Asia with the highest enrolments have functioned with limitations.
##
## Summary :-
## The number of enrolments in public schools does not seem to contibute to the closure of schools,
## irrespective of the schools' or students' access to online or other alternate methods of eductaion.
## Inference
## 1. The size of the circles indicate the Income level.
## 2. Schools in the lower-middle-income countries have been closed, though the enrolment count
## is highest in these countries.
## This is contradictory to the fact about the inherent 'digital divide' in the developing/under-developed countries.
## Data about the size and facilities of these schools may have to be analysed to understand
## if the schools were closed, because it was not possible to follow the said norms to contain the pandemic.
## 3. Schools continued to be 'open with limitations' in the upper middle income & high income countries.
## Data on the economic health of these countries may have to be analysed, to derive at a conclusion.
##
## Summary :-
## The income levels vs school closure is inconclusive.
## Inference
## 1. Pre-Primary enrolments are lowest across all income levels.
## It is common to enrol kids in the school, directly in the Primary level.
## 2. Starting from the Primary level, the number of enrolments have reduced in the subsequent school levels,
## across all the income categories.
## 3. Enrolment across all the school levels are highest in the lower-middle-income category.
##
## Summary :-
## 1. Low income category can be excluded, owing to affordability to school education.
## 2. Considering only the high-income, upper-middle-income & lower-middle-income categories,
## there is an 'inverse co-relation' between the income levels and the enrolment in schools.
## 3. The dropouts at each school level also seem to have an 'inverse co-relation' with the income level.
## Inference :-
## 1. East Asian countries that fall under the upper-middle-income level have significantly more enrolments
## and schools functioning with limitations.
##
## 2.Except the East Asian countries, the enrolments are significantly lower in the other
## upper-middle-income countries.
##
## 3. Similarly, expect the North American countries, the rest of the countries in the high-income level
## have significantly lower enrolment in public schools.
##
## 4. Schools in the Saharan African countries, irrespective of the income levels were either
## open with limitations or closed only in selected areas.
## This could be an indicator of non-access to the digital or alternate methods of education
## or lesser impact of the pandemic.
##
## 5. All the schools, across all the South Asian countries and across all the income levels, have remained closed.
## 1. The enrolment in public schools is not directly related to the closure of schools.
## 2. The enrolment in public schools seem to have an inverse-corelation with the income levels of the country.
## 3. The relationship between income levels and school closure is inconclusive.
## Data on the economic health of the countries are to be studied to understand the underlying factors.
## 4. There is no significant relationship between the geographic location and the school closures.
## Subject Matter Expert, Data Visualization (IIT Madras)
## Program Manager, CTM (Certificate Programme in Technology & Management)
## (CTM - a joint programme by IIT Madras & IIM Bangalore - https://ctm-iitm.iimbx.edu.in/)